A Comprehensive Review of Deep Learning and Swarm Intelligence-Based Energy-Efficient Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have become one of the most significant technologies in modern Internet of Things (IoT) environments due to their ability to monitor physical and environmental conditions in real time. WSNs are widely used in healthcare systems, military surveillance, indus-trial automation, environmental monitoring, smart agriculture, disaster management, and intelligent transportation systems. However, sensor nodes operate with limited battery resources, constrained memory capacity, low computational capability, and short communication range. Therefore, energy-efficient routing remains one of the major research challenges in Wireless Sensor Networks. Traditional routing protocols such as LEACH, PEGASIS, TEEN, and HEED mainly focus on clustering and multi-hop communication mechanisms to reduce communication overhead and prolong network lifetime. Nevertheless, these protocols suffer from limitations including random cluster head selection, uneven energy dissipation, packet loss, congestion, poor scalability, and inability to adapt to dynamic network conditions. Artificial Intelligence (AI)-based routing optimization tech-niques have recently emerged as effective solutions for over-coming these limitations. Deep Learning (DL) methods provide intelligent route prediction, adaptive communication, traffic fore-casting, congestion estimation, and dynamic routing optimization capabilities. Swarm Intelligence (SI) algorithms imitate collective biological behaviors to discover optimal communication paths with minimum energy consumption. This paper presents a detailed review of deep learning and swarm intelligence-based energy-efficient routing techniques in Wireless Sensor Networks. Various techniques including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Reinforcement Learn-ing (DRL), Graph Neural Networks (GNN), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Firefly Algorithm (FA) are discussed comprehensively. Hybrid AI-based routing approaches combining deep learning and swarm intelligence are also analyzed. Further-more, comparative analysis based on energy efficiency, scalability, latency, throughput, packet delivery ratio, and computational complexity is presented. The paper also discusses security chal-lenges, edge computing integration, IoT convergence, and future research directions including federated learning, explainable AI, blockchain-enabled routing security, and quantum-inspired optimization.